
from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	42




class 					lowerCamelCase__				(							__lowercase	,		__lowercase):




	'''simple docstring'''




	@register_to_config
	def __init__(   self						:Optional[Any]	,  a						:int = 3_2	,  a						:int = 6_4	,  a						:int = 2_0	,  a						:int = 7_6_8	,  a						:Dict=7_7	,  a						:Optional[Any]=4	,  a						:float = 0.0	,  a						:str = "silu"	,  a						:Optional[str] = None	,  a						:Optional[str] = None	,  a						:Optional[str] = "linear"	,  a						:Optional[str] = "prd"	,  a						:Optional[int] = None	,  a						:Optional[int] = None	,  a						:Optional[int] = None	,  )						->					Optional[int]:
								super().__init__()
								__UpperCamelCase      :  int       =   num_attention_heads
								__UpperCamelCase      :  List[str]       =   attention_head_dim
								__UpperCamelCase      :  Optional[int]       =   num_attention_heads * attention_head_dim
								__UpperCamelCase      :  Dict       =   additional_embeddings

								__UpperCamelCase      :  Tuple       =   time_embed_dim or inner_dim
								__UpperCamelCase      :  List[str]       =   embedding_proj_dim or embedding_dim
								__UpperCamelCase      :  Union[str, Any]       =   clip_embed_dim or embedding_dim

								__UpperCamelCase      :  List[str]       =   Timesteps(a	,  a	,  0		)
								__UpperCamelCase      :  Optional[Any]       =   TimestepEmbedding(a	,  a	,  out_dim=a	,  act_fn=a		)

								__UpperCamelCase      :  Tuple       =   nn.Linear(a	,  a		)

								if embedding_proj_norm_type is None:
															__UpperCamelCase      :  Union[str, Any]       =   None
								elif embedding_proj_norm_type == "layer":
															__UpperCamelCase      :  Optional[Any]       =   nn.LayerNorm(a		)
								else:
															raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'		)

								__UpperCamelCase      :  Any       =   nn.Linear(a	,  a		)

								if encoder_hid_proj_type is None:
															__UpperCamelCase      :  Union[str, Any]       =   None
								elif encoder_hid_proj_type == "linear":
															__UpperCamelCase      :  List[str]       =   nn.Linear(a	,  a		)
								else:
															raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'		)

								__UpperCamelCase      :  List[Any]       =   nn.Parameter(torch.zeros(1	,  num_embeddings + additional_embeddings	,  a		)		)

								if added_emb_type == "prd":
															__UpperCamelCase      :  Union[str, Any]       =   nn.Parameter(torch.zeros(1	,  1	,  a		)		)
								elif added_emb_type is None:
															__UpperCamelCase      :  Optional[int]       =   None
								else:
															raise ValueError(
															    f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'		)

								__UpperCamelCase      :  Tuple       =   nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            a	,  a	,  a	,  dropout=a	,  activation_fn="gelu"	,  attention_bias=a	,  )
								        for d in range(a		)
								    ]		)

								if norm_in_type == "layer":
															__UpperCamelCase      :  Optional[Any]       =   nn.LayerNorm(a		)
								elif norm_in_type is None:
															__UpperCamelCase      :  Tuple       =   None
								else:
															raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'		)

								__UpperCamelCase      :  Dict       =   nn.LayerNorm(a		)

								__UpperCamelCase      :  Tuple       =   nn.Linear(a	,  a		)

								__UpperCamelCase      :  int       =   torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]	,  -10000.0		)
								causal_attention_mask.triu_(1		)
								__UpperCamelCase      :  Any       =   causal_attention_mask[None, ...]
								self.register_buffer("causal_attention_mask"	,  a	,  persistent=a		)

								__UpperCamelCase      :  Any       =   nn.Parameter(torch.zeros(1	,  a		)		)
								__UpperCamelCase      :  Optional[Any]       =   nn.Parameter(torch.zeros(1	,  a		)		)







	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def   _lowerCamelCase							(   self						:str		)						->					Dict[str, AttentionProcessor]:
								__UpperCamelCase      :  Dict       =   {}

								def fn_recursive_add_processors(a						:str	,  a						:torch.nn.Module	,  a						:Dict[str, AttentionProcessor]		):
															if hasattr(a	,  "set_processor"		):
																						__UpperCamelCase      :  Any       =   module.processor

															for sub_name, child in module.named_children():
																						fn_recursive_add_processors(f'{name}.{sub_name}'	,  a	,  a		)

															return processors

								for name, module in self.named_children():
															fn_recursive_add_processors(a	,  a	,  a		)

								return processors







	def   _lowerCamelCase							(   self						:Tuple	,  a						:Union[AttentionProcessor, Dict[str, AttentionProcessor]]		)						->					Any:
								__UpperCamelCase      :  Dict       =   len(self.attn_processors.keys()		)

								if isinstance(a	,  a		) and len(a		) != count:
															raise ValueError(
															    f'A dict of processors was passed, but the number of processors {len(a		)} does not match the'
															    f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'		)

								def fn_recursive_attn_processor(a						:str	,  a						:torch.nn.Module	,  a						:Any		):
															if hasattr(a	,  "set_processor"		):
																						if not isinstance(a	,  a		):
																													module.set_processor(a		)
																						else:
																													module.set_processor(processor.pop(f'{name}.processor'		)		)

															for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(f'{name}.{sub_name}'	,  a	,  a		)

								for name, module in self.named_children():
															fn_recursive_attn_processor(a	,  a	,  a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								self.set_attn_processor(AttnProcessor()		)







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:Optional[int]	,  a						:Union[torch.Tensor, float, int]	,  a						:torch.FloatTensor	,  a						:Optional[torch.FloatTensor] = None	,  a						:Optional[torch.BoolTensor] = None	,  a						:bool = True	,  )						->					List[str]:
								__UpperCamelCase      :  Any       =   hidden_states.shape[0]

								__UpperCamelCase      :  Any       =   timestep
								if not torch.is_tensor(a		):
															__UpperCamelCase      :  Optional[Any]       =   torch.tensor([timesteps]	,  dtype=torch.long	,  device=hidden_states.device		)
								elif torch.is_tensor(a		) and len(timesteps.shape		) == 0:
															__UpperCamelCase      :  int       =   timesteps[None].to(hidden_states.device		)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								__UpperCamelCase      :  Union[str, Any]       =   timesteps * torch.ones(a	,  dtype=timesteps.dtype	,  device=timesteps.device		)

								__UpperCamelCase      :  Union[str, Any]       =   self.time_proj(a		)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might be fp16, so we need to cast here.
								__UpperCamelCase      :  Union[str, Any]       =   timesteps_projected.to(dtype=self.dtype		)
								__UpperCamelCase      :  Tuple       =   self.time_embedding(a		)

								if self.embedding_proj_norm is not None:
															__UpperCamelCase      :  Optional[int]       =   self.embedding_proj_norm(a		)

								__UpperCamelCase      :  Dict       =   self.embedding_proj(a		)
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
															__UpperCamelCase      :  List[Any]       =   self.encoder_hidden_states_proj(a		)
								elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
															raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"		)

								__UpperCamelCase      :  Tuple       =   self.proj_in(a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.positional_embedding.to(hidden_states.dtype		)

								__UpperCamelCase      :  Optional[Any]       =   []
								__UpperCamelCase      :  List[Any]       =   0

								if encoder_hidden_states is not None:
															additional_embeds.append(a		)
															additional_embeddings_len += encoder_hidden_states.shape[1]

								if len(proj_embeddings.shape		) == 2:
															__UpperCamelCase      :  Tuple       =   proj_embeddings[:, None, :]

								if len(hidden_states.shape		) == 2:
															__UpperCamelCase      :  Optional[Any]       =   hidden_states[:, None, :]

								__UpperCamelCase      :  List[str]       =   additional_embeds + [
								    proj_embeddings,
								    time_embeddings[:, None, :],
								    hidden_states,
								]

								if self.prd_embedding is not None:
															__UpperCamelCase      :  Optional[int]       =   self.prd_embedding.to(hidden_states.dtype		).expand(a	,  -1	,  -1		)
															additional_embeds.append(a		)

								__UpperCamelCase      :  Union[str, Any]       =   torch.cat(
								    a	,  dim=1	,  )

								# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
								__UpperCamelCase      :  Union[str, Any]       =   additional_embeddings_len + proj_embeddings.shape[1] + 1
								if positional_embeddings.shape[1] < hidden_states.shape[1]:
															__UpperCamelCase      :  str       =   F.pad(
															    a	,  (
															        0,
															        0,
															        additional_embeddings_len,
															        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
															    )	,  value=0.0	,  )

								__UpperCamelCase      :  str       =   hidden_states + positional_embeddings

								if attention_mask is not None:
															__UpperCamelCase      :  List[str]       =   (1 - attention_mask.to(hidden_states.dtype		)) * -10000.0
															__UpperCamelCase      :  List[Any]       =   F.pad(a	,  (0, self.additional_embeddings)	,  value=0.0		)
															__UpperCamelCase      :  Any       =   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
															__UpperCamelCase      :  int       =   attention_mask.repeat_interleave(self.config.num_attention_heads	,  dim=0		)

								if self.norm_in is not None:
															__UpperCamelCase      :  Tuple       =   self.norm_in(a		)

								for block in self.transformer_blocks:
															__UpperCamelCase      :  Optional[int]       =   block(a	,  attention_mask=a		)

								__UpperCamelCase      :  int       =   self.norm_out(a		)

								if self.prd_embedding is not None:
															__UpperCamelCase      :  str       =   hidden_states[:, -1]
								else:
															__UpperCamelCase      :  Tuple       =   hidden_states[:, additional_embeddings_len:]

								__UpperCamelCase      :  str       =   self.proj_to_clip_embeddings(a		)

								if not return_dict:
															return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=a		)







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:Optional[int]		)						->					Optional[Any]:
								__UpperCamelCase      :  str       =   (prior_latents * self.clip_std) + self.clip_mean
								return prior_latents

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    :   Tuple						=			{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   int						=			[
		    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LukeForEntityClassification',
		    'LukeForEntityPairClassification',
		    'LukeForEntitySpanClassification',
		    'LukeForMultipleChoice',
		    'LukeForQuestionAnswering',
		    'LukeForSequenceClassification',
		    'LukeForTokenClassification',
		    'LukeForMaskedLM',
		    'LukeModel',
		    'LukePreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		lowercase    :   Any						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)